Before launching into ultrasound research, it is important to recall that the ultimate
goal is to provide the clinician with the best possible information needed to make an
accurate diagnosis. Ultrasound images are inherently affected by speckle noise, which
is due to image formation under coherent waves. Thus, it appears to be sensible
to reduce speckle artifacts before performing image analysis, provided that image
texture that might distinguish one tissue from another is preserved.
The main goal of this thesis was the development of novel speckle suppression
methods from medical ultrasound images in the multiscale wavelet domain. We
started by showing, through extensive modeling, that the subband decompositions of
ultrasound images have significantly non-Gaussian statistics that are best described
by families of heavy-tailed distributions such as the alpha-stable. Then, we developed
Bayesian estimators that exploit these statistics. We used the alpha-stable
model to design both the minimum absolute error (MAE) and the maximum a posteriori
(MAP) estimators for alpha-stable signal mixed in Gaussian noise. The resulting
noise-removal processors perform non-linear operations on the data and we
relate this non-linearity to the degree of non-gaussianity of the data. We compared
our techniques to classical speckle filters and current state-of-the-art soft and hard
thresholding methods applied on actual ultrasound medical images and we quantified
the achieved performance improvement.
Finally, we have shown that our proposed processors can find application in other
areas of interest as well, and we have chosen as an illustrative example the case of
synthetic aperture radar (SAR) images.